{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Prepare the Functions\n",
    "exec(open('Step_01_WealthIncomeDistribution.py').read())\n",
    "Var_Income = 'Income_Market'\n",
    "DS[Var_Income] = DS[Var_Income].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Fun_UnitStat(Var):\n",
    "    # Var = 'NetWorth_Liquid'\n",
    "\n",
    "    ## by Quantiles\n",
    "    DistbyQuant = Dist_byQuantile(DS,Var,'StatWeight',list(np.linspace(0.01,0.99,num=99)))\n",
    "    TempData = pd.DataFrame({'AccQW_up':[1,0],'AccQQ_up':[1,0], \\\n",
    "                             'QQ':[DS[Var].min(),DS[Var].max()],'Quant':[0,1], \\\n",
    "                             'AccQW_down':[0,1,],'AccQQ_down':[0,1]})\n",
    "    DistByQuant = DistbyQuant.append(TempData).sort_values(by='Quant').reset_index(drop=True)\n",
    "    \n",
    "    ## by Histogram Grids\n",
    "    HistList = [*list(np.linspace(-3*1e4,0-1e3,4)),*list(np.linspace(1e3,700*1e4,36))]\n",
    "    DistByHist = Dist_byHistogram(DS,Var,'StatWeight',bins=50)\n",
    "\n",
    "    ## Summary Statistics\n",
    "    QuantFun = interp1d(DistByQuant['AccQW_down'],DistByQuant['QQ'])\n",
    "    AccQWFun = interp1d(DistByQuant['QQ'],DistByQuant['AccQW_down'])\n",
    "    QuantList = [0.01,0.05,0.10,0.25,0.5,0.75,0.90,0.95,0.99]\n",
    "    SumStat = pd.Series(QuantFun(QuantList),index=[str(round(xx*100))+'%' for xx in QuantList])\n",
    "    SumStat['Min'] = DS[Var].min()\n",
    "    SumStat['Max'] = DS[Var].max()\n",
    "    SumStat['Mean'] = QW_Mean(DS,Var,'StatWeight')\n",
    "    SumStat['Gini'] = Dist_GiniCoef(DS,Var,'StatWeight')\n",
    "    \n",
    "    return {'SumStat':SumStat, 'DistByQuant': DistByQuant, 'DistByHist': DistByHist, 'QuantFun': QuantFun, 'AccQWFun': AccQWFun}\n",
    "\n",
    "def Fun_UnitStat_2(Var):\n",
    "    # Var = 'NetWorth_Liquid'\n",
    "\n",
    "    ## by Quantiles\n",
    "    DistbyQuant = Dist_byQuantile(DS,Var,'StatWeight',list(np.linspace(0.01,0.99,num=99)))\n",
    "    TempData = pd.DataFrame({'AccQW_up':[1,0],'AccQQ_up':[1,0], \\\n",
    "                             'QQ':[DS[Var].min(),DS[Var].max()],'Quant':[0,1], \\\n",
    "                             'AccQW_down':[0,1,],'AccQQ_down':[0,1]})\n",
    "    DistByQuant = DistbyQuant.append(TempData).sort_values(by='Quant').reset_index(drop=True)\n",
    "\n",
    "    ## Summary Statistics\n",
    "    QuantFun = interp1d(DistByQuant['AccQW_down'],DistByQuant['QQ'])\n",
    "    AccQWFun = interp1d(DistByQuant['QQ'],DistByQuant['AccQW_down'])\n",
    "    QuantList = [0.01,0.05,0.10,0.25,0.5,0.75,0.90,0.95,0.99]\n",
    "    SumStat = pd.Series(QuantFun(QuantList),index=[str(round(xx*100))+'%' for xx in QuantList])\n",
    "    SumStat['Min'] = DS[Var].min()\n",
    "    SumStat['Max'] = DS[Var].max()\n",
    "    SumStat['Mean'] = QW_Mean(DS,Var,'StatWeight')\n",
    "    SumStat['Gini'] = Dist_GiniCoef(DS,Var,'StatWeight')\n",
    "    \n",
    "    return {'SumStat':SumStat, 'DistByQuant': DistByQuant, 'QuantFun': QuantFun, 'AccQWFun': AccQWFun}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Distribution of Income\n",
    "Stat = {}\n",
    "Stat['Income'] = Fun_UnitStat('Income_Market')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1%      -7.130131e-53\n",
       "5%     -4.909825e-145\n",
       "10%      1.199061e+03\n",
       "25%      1.951844e+04\n",
       "50%      5.251388e+04\n",
       "75%      1.000000e+05\n",
       "90%      1.600059e+05\n",
       "95%      2.000163e+05\n",
       "99%      3.651454e+05\n",
       "Min     -3.400000e+04\n",
       "Max      2.600000e+06\n",
       "Mean     7.376022e+04\n",
       "Gini     4.732718e-01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Income']['SumStat']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fraction of Negative Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.03997255)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Income']['AccQWFun'](0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Details of Distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-2e07d590943d>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TempDS['QQ_mid'] = TempDS['QQ_mid'].round()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='QQ_mid'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = fig.add_subplot(1,2,1)\n",
    "plt.plot(Stat['Income']['DistByQuant']['Quant'],Stat['Income']['DistByQuant'][['Quant','AccQQ_down']])\n",
    "plt.hlines(0,0,1,colors='k',linestyles='solid',alpha=0.2)\n",
    "ax = fig.add_subplot(1,2,2)\n",
    "TempDS = Stat['Income']['DistByHist']\n",
    "TempDS = TempDS[TempDS['QW']>1e-3]\n",
    "TempDS['QQ_mid'] = TempDS['QQ_mid'].round()\n",
    "TempDS.plot(ax=ax,kind='bar',x='QQ_mid',y='QW')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Net Wealth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Var = 'NetWorth'\n",
    "Stat[Var] = Fun_UnitStat('NetWorth_G')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1%     -3.111663e+04\n",
       "5%     -8.897030e+02\n",
       "10%     2.661711e+03\n",
       "25%     4.328245e+04\n",
       "50%     3.021931e+05\n",
       "75%     8.594532e+05\n",
       "90%     1.671235e+06\n",
       "95%     2.384727e+06\n",
       "99%     5.185530e+06\n",
       "Min    -1.110000e+06\n",
       "Max     2.728450e+07\n",
       "Mean    6.762201e+05\n",
       "Gini    3.354429e-01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat[Var]['SumStat']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fraction of Negative Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.05777711)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat[Var]['AccQWFun'](0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Details of Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-ca3e317c01ef>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TempDS['QQ_mid'] = np.round(TempDS['QQ_mid']/1e3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='QQ_mid'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = fig.add_subplot(1,2,1)\n",
    "plt.plot(Stat[Var]['DistByQuant']['Quant'],Stat[Var]['DistByQuant'][['Quant','AccQQ_down']])\n",
    "plt.hlines(0,0,1,colors='k',linestyles='solid',alpha=0.2)\n",
    "ax = fig.add_subplot(1,2,2)\n",
    "TempDS = Stat[Var]['DistByHist']\n",
    "TempDS = TempDS[TempDS['QW']>1e-3]\n",
    "TempDS['QQ_mid'] = np.round(TempDS['QQ_mid']/1e3)\n",
    "TempDS.plot(ax=ax,kind='bar',x='QQ_mid',y='QW')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Liquid Wealth "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Var = 'NetWorth_Liquid'\n",
    "Stat[Var] = Fun_UnitStat('NetWorth_Liquid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1%     -2.970903e+04\n",
       "5%     -9.731222e+03\n",
       "10%    -4.097595e+03\n",
       "25%     2.181746e+01\n",
       "50%     4.002108e+03\n",
       "75%     2.200749e+04\n",
       "90%     1.151471e+05\n",
       "95%     2.893814e+05\n",
       "99%     1.183049e+06\n",
       "Min    -1.090500e+05\n",
       "Max     1.850000e+07\n",
       "Mean    6.680452e+04\n",
       "Gini    5.993683e-02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat[Var]['SumStat']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fraction of Negative Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.21998819)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat[Var]['AccQWFun'](0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Details of Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-14-ca3e317c01ef>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TempDS['QQ_mid'] = np.round(TempDS['QQ_mid']/1e3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='QQ_mid'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = fig.add_subplot(1,2,1)\n",
    "plt.plot(Stat[Var]['DistByQuant']['Quant'],Stat[Var]['DistByQuant'][['Quant','AccQQ_down']])\n",
    "plt.hlines(0,0,1,colors='k',linestyles='solid',alpha=0.2)\n",
    "ax = fig.add_subplot(1,2,2)\n",
    "TempDS = Stat[Var]['DistByHist']\n",
    "TempDS = TempDS[TempDS['QW']>1e-3]\n",
    "TempDS['QQ_mid'] = np.round(TempDS['QQ_mid']/1e3)\n",
    "TempDS.plot(ax=ax,kind='bar',x='QQ_mid',y='QW')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     AccQW_up  AccQQ_up            QQ  Quant  AccQQ_down  AccQW_down\n",
      "0    1.000000  1.000000 -1.090500e+05   0.00    0.000000    0.000000\n",
      "1    0.990069  1.007335 -2.977768e+04   0.01   -0.007335    0.009931\n",
      "2    0.980191  1.010949 -1.995317e+04   0.02   -0.010949    0.019809\n",
      "3    0.970085  1.013600 -1.499628e+04   0.03   -0.013600    0.029915\n",
      "4    0.960052  1.015637 -1.174713e+04   0.04   -0.015637    0.039948\n",
      "..        ...       ...           ...    ...         ...         ...\n",
      "96   0.040007  0.680002  3.535484e+05   0.96    0.319998    0.959993\n",
      "97   0.030048  0.618696  4.699024e+05   0.97    0.381304    0.969952\n",
      "98   0.020040  0.538027  6.301291e+05   0.98    0.461973    0.979960\n",
      "99   0.010018  0.413815  1.151854e+06   0.99    0.586185    0.989982\n",
      "100  0.000000  0.000000  1.850000e+07   1.00    1.000000    1.000000\n",
      "\n",
      "[101 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "Stat['NetWorth_Liquid']['DistByQuant']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wealth-Income Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.905698471819156"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QW_Mean(DS,Var_Wealth,'StatWeight')/QW_Mean(DS,Var_Income,'StatWeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Liquid Net Worth - Income Ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ratio between Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.905698471819156"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QW_Mean(DS,'NetWorth_Liquid','StatWeight')/QW_Mean(DS,'Income_Market','StatWeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ratio between Median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07621047898292432"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['NetWorth_Liquid']['QuantFun'](0.5)/Stat['Income']['QuantFun'](0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "DS['Ratio_NetWorthLiquid_Income_1'] = DS['NetWorth_Liquid']/DS['Income_Market']\n",
    "DS['Ratio_NetWorthLiquid_Income_2'] = DS['NetWorth_Liquid']/QW_Mean(DS,'Income_Market','StatWeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_1'] = Fun_UnitStat_2('Ratio_NetWorthLiquid_Income_1')\n",
    "Stat['Ratio_NetWorthLiquid_Income_2'] = Fun_UnitStat_2('Ratio_NetWorthLiquid_Income_2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.08811397)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_1']['QuantFun'](0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.05425835)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Stat['Ratio_NetWorthLiquid_Income_2']['QuantFun'](0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fraction of HtM Households"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38173609609331516"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DS['Flag_HtM_1'] = DS['Ratio_NetWorthLiquid_Income_1']<1/12/2\n",
    "QW_Mean(DS,'Flag_HtM_1','StatWeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46775017415863607"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DS['Flag_HtM_2'] = DS['Ratio_NetWorthLiquid_Income_2']<1/12/2\n",
    "QW_Mean(DS,'Flag_HtM_2','StatWeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37969156381212643"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DS['Flag_HtM'] = (DS['NetWorth_Liquid']<DS['Income_Market']/12/2)\n",
    "QW_Mean(DS,'Flag_HtM','StatWeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
